CN103577881B - Based on client's mistake, the peak load shedding effect evaluation method of ordered electric - Google Patents

Abstract

The present invention proposes a method for evaluating the effect of customer error, peak avoidance and electricity curtailment based on orderly electricity consumption, using three major indexes: peak avoidance index, scheduling index, and time-staggered index. Among them, the peak avoidance index represents the effect of the user's implementation of the peak avoidance measures to limit the power; the adjustment index represents the effect of the user's implementation of the adjustment and rest measures; The advantage of the present invention is that these indexes, as a means to evaluate the effects of customer faults and peak avoidance measures, can represent the comprehensive impact of customers' power-limiting behavior on the power grid, society, and customers themselves, and further improve the accuracy of evaluating the user's power-limiting effects. sex, fairness.

Description

Evaluation method of customer error, peak avoidance and curtailment effect based on orderly electricity consumption

technical field

The invention relates to a method for evaluating the effect of customer error and peak-avoiding power rationing based on orderly power consumption, which belongs to the field of electric power.

Background technique

The contradiction between power supply and demand will inevitably occur in the process of my country's economic development. At present, China's electricity market is in the process of reform, and the lack of conditions prevents the power demand side management from fully exerting its effects, and cannot effectively solve the problem of power supply and demand balance. In the face of tight power supply and demand and the reality of power supply gaps, there is only an orderly power consumption work.

Compiling an orderly electricity consumption plan is the core of the orderly electricity use work. During the preparation of the plan, all parts of the country mainly rely on the various gaps in the power supply and rely on manual experience to roughly analyze the production characteristics and electricity load characteristics of the power grid and the city's users. , and then take different measures such as peak shifting, peak avoidance, power limiting, and power pulling to formulate an orderly power consumption decision-making plan. In the preparation of the plan for orderly electricity use, how to comprehensively and accurately evaluate the effect of each customer's implementation of orderly use of electricity is the focus of the entire plan.

At present, the evaluation of the effect of each customer's implementation of orderly electricity use is mainly based on the "Orderly Use of Electricity Management Measures" formulated by the National Development and Reform Commission and the State Grid Corporation of China. The usual practice is to first follow the principle of "limited warranty" in the "Orderly Power Consumption Management Measures", and according to the nature of the customer's "industry" of electricity consumption, through communication and manual experience judgment, customers are divided into high energy-consuming users and centralized maintenance. Users, manufacturing, non-industrial users, and guaranteed power supply users are divided into five categories. Secondly, for the above five types of users, the size of the customer load limit is used as the evaluation of the effect of making mistakes and avoiding peaks.

Orderly electricity consumption is a multi-conditional, multi-objective decision-making problem that needs to be considered from the perspectives of power grids, governments, and corporate customers. The existing evaluation model uses the customer's limitable load as the evaluation standard, with single constraints and no comprehensive consideration of customers. multi-dimensional information, the evaluation results are not comprehensive and fair. In the existing mode, the user's power rationing effect is mainly judged based on manual experience, which leads to bargaining in the process of evaluating the effect of customer faults and off-peak power rationing, and affects the efficiency of formulating plans.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies in the prior art, and to provide a method for evaluating the effect of customer faults and peak avoidance power cuts based on orderly power consumption, which can quantitatively calculate the effect of orderly power use by customers, which includes the peak avoidance index , time-off index, and time-staggered index, these indexes are used in the preparation of orderly electricity consumption plans by power supply enterprises to improve the implementation effect of orderly electricity use by power grid enterprises.

According to the technical solution provided by the present invention, the method for evaluating the effect of customer faults and peak avoidance power cuts based on orderly power consumption uses the peak avoidance index to represent the effect of the user's implementation of peak avoidance measures and power cuts, and uses the scheduling index to characterize the user The size of the electricity restriction effect of the implementation of the time-staggered measures is used to represent the size of the electricity restriction effect of the user's implementation of the time-staggered measures; the specific calculation process is divided into 4 steps.

Step 1. Calculating the relevant parameters related to the peak avoidance index, the holiday index, and the time-staggered index are as follows:

1.1) Draw a typical customer curve: select the load data of the customer for at least 3 months in the near future, perform superimposed average calculation on the daily load curve during this period, and form a typical customer load curve by fitting;

1.2) Calculate the ratio of the customer's annual production added value to the annual electricity consumption to obtain the customer's unit electricity production added value;

1.3) Calculate the ratio of the customer's annual profit and tax to the government and the annual electricity consumption to obtain the customer's unit electricity profit and tax;

1.4) Calculate the ratio of the average value of the customer load to the standard deviation from 8:00 to 22:00 on a typical day to obtain the customer load fluctuation rate; the calculation formula of the load fluctuation rate is as follows:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}<span\; class="notranslate">\; =</span>\frac{\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$

In the formula: f _l - load fluctuation rate;

P _i - typical load value at the i-th time point;

σ - standard deviation of loading;

μ - the mean value of the load;

N - the number of said time points;

1.5) Calculation of customer security load: In statistical step 1.1, the customer selects the actual load data within the time period, and calculates the arithmetic mean value of the smallest 200 load points. The formula is as follows:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 200</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 200</span>}}$

In the formula: p _sl - security load;

p _mini - the ith smallest load point;

1.6) Calculate the customer's limitable load: According to the customer's typical load curve drawn in step 1.1, the calculation formula is as follows:

P _ll -mor=(P _pl -mor-P _sl )×δ _p

P _ll -mid=(P _pl -mid-P _sl )×δ _p

P _ll -eve=(P _pl -ev _e -P _sl )×δ _p

In the formula: P _ll-mo r, P _ll-mid , P _ll-eve represent the limitable loads of morning peak, waist peak and evening peak respectively;

P _sl - security load;

P _pl-mor , P _pl-mid , and P _pl-eve represent the peak loads during morning peak, waist peak, and evening peak, respectively;

δ _p - simultaneity rate;

Step 2. Calculation of avoidance index:

2.1), calculate the initial value of the customer peak avoidance index according to the customer peak avoidance index parameters obtained in step 1, the formula is as follows:

In the formula: P _pl-mor , P _pl-mid , and P _ll-eve represent the limitable loads in the early peak, mid-peak, and late peak periods, respectively;

E _q - the added value of electricity production per unit;

T _q - profit and tax per unit of electricity;

f _l - load fluctuation rate;

P _q - customer electricity price;

2.2) Organize the initial value data of peak avoidance index of all customers, use K-means cluster analysis method, take K=5, and divide the initial value of peak avoidance index into 5 levels, which are excellent, good, moderate, general, and poor;

2.3) Judging whether the nature of the customer's production is continuous;

2.4) The initial value of the peak avoidance index of the non-continuous user is reduced by one gear, and the peak avoidance index is calculated; for the continuous user, the peak avoidance index value remains unchanged;

Step 3. Calculation of time off index:

3.1) Calculate the initial value of the rest index, the formula is as follows: initial value of the rest index = limitable load;

3.2) Organize the initial value data of all customers' rest index, use K-means cluster analysis method, take K=5, divide the initial value of the rest index into 5 grades, which are excellent, good, moderate, general, and poor;

3.3) According to the customer's typical load curve, calculate the customer's weekly rest rate, the formula is as follows:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; l</span>}}}$

In the formula: Q _wd - the arithmetic mean of the total load from Monday to Friday;

Q _We - the arithmetic mean of the total load on Saturday and Sunday;

Q _sl - the total load of security load in one day;

3.4) The initial value of the customer's rest index with a weekly rest rate greater than 0.3 and less than 0.7 will be reduced by two steps; the initial value of the client's rest index with a weekly rest rate greater than or equal to 0.7 will be reduced by one step, and the weekly rest rate will be less than or equal to The initial value of the customer adjustment index of 0.3 remains unchanged;

Step 4. Calculation of time error index:

4.1) Calculate the initial value of the timing index value, the formula is as follows: initial value of the timing index = limitable load;

4.2) Statistically calculate the initial value of the timing index of all customers, use the K-means cluster analysis method, take K=5, and divide the timing index into 5 grades, which are excellent, good, moderate, general, and poor;

4.3) The result of the timing index will not be adjusted, and it will be the same as the initial value.

The simultaneity rate δ _p is generally taken as 0.8.

The morning peak, waist peak, and evening peak hours are respectively: 8:00-12:00, 12:00-17:00, and 17:00-22:00.

The advantage of the present invention is that: a set of evaluation indexes based on the implementation effect of customer faults and peak avoidance power reduction measures based on orderly power consumption is proposed. The comprehensive impact of electricity behavior on the power grid, society, and customers themselves will further improve the accuracy and fairness of evaluating the effect of user electricity curtailment.

Description of drawings

Figure 1 is a frame diagram of the value indicator system for orderly electricity consumption by customers.

Figure 2 is a flow chart for calculating the evaluation index of customer fault and peak avoidance measures based on orderly electricity consumption.

Fig. 3 is a flowchart of a method for judging production continuity of a customer.

detailed description

The present invention will be further described below in conjunction with drawings and embodiments.

The present invention establishes an evaluation system for the implementation effect of orderly electricity consumption by customers. The system is divided into three levels, and the structure of the evaluation system is shown in FIG. 1 .

(1) Target layer: the effect of the customer's implementation of peak shifting and avoiding peak measures;

(2) Index layer: implementation effect evaluation indicators of staggered and peak-avoidance measures, including peak-avoidance index, scheduling index, and time-staggered index;

(3) Parameter layer: related parameters related to the peak avoidance index, the time-off index, and the time-staggered index.

The invention takes the four elements of power grid power gap, economic influence, electricity influence and tax influence as constraint conditions, builds a mathematical model, and proposes three major indexes: peak avoidance index, time-off index, and time-staggered index. Among them, the peak avoidance index represents the effect of the user's implementation of the peak avoidance measures to limit the power; the adjustment index represents the effect of the user's implementation of the adjustment and rest measures;

The specific calculation process of the above index is divided into four steps, as shown in Figure 2.

Step 1. Calculation of relevant parameters related to peak avoidance index, time-off index, and time-staggered index.

1.1) Draw a typical customer curve. Select the load data of the customer for at least three recent months (excluding holidays), and perform superposition and average calculation on the daily load curve during this period, and fit the typical load curve of the customer.

1.2) Calculate the ratio of the customer's annual production added value to the annual electricity consumption to obtain the customer's unit electricity production added value.

1.3) Calculate the ratio of the customer's annual profit and tax to the government and the annual electricity consumption to obtain the customer's unit electricity profit and tax.

1.4) Calculate the ratio of the average value of customer load to the standard deviation from 8:00 to 22:00 on a typical day to obtain the customer load fluctuation rate. It is also possible to select several typical days as samples, calculate the load fluctuation rate values of the day respectively, and then calculate the arithmetic mean value as the customer's load fluctuation rate. The calculation formula of load fluctuation rate is as follows:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}<span\; class="notranslate">\; =</span>\frac{\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$

In the formula: f _l ——load fluctuation rate;

P _i ——typical load value at the i-th time point;

σ——Standard deviation of load;

μ—the mean value of the load.

1.5) Calculate customer security load. In the statistical step 1.1, the customer selects the actual load data within the time period, and calculates the arithmetic mean value of the smallest 200 load points. The formula is as follows:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 200</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 200</span>}}$

where: p _sl — security load;

p _{mini ——the} i-th smallest load point.

1.6) Calculate the customer's limitable load. According to the customer's typical load curve drawn in step 1.1, the calculation formula is as follows:

P _pl-mor = (P _pl-mor -P _sl )×δ _p

P _ll-mid = (P _pl-mid -P _sl )×δ _p

P _ll-eve = (P _pl-eve -P _sl )×δ _p

In the formula: P _ll-mor , P _pl-mid , P _{ll-eve —represent} the limitable loads in the early peak, mid-peak and late peak periods respectively;

P _sl — security load;

P _pl-mor , P _pl-mid , P _{pl-eve —represent} the peak loads during the morning peak, waist peak, and evening peak respectively;

δ _p ——simultaneity rate, generally 0.8.

Table 1 Arrangement of orderly power consumption peak hours

Step 2. Calculation of avoidance index.

2.1), according to the customer peak avoidance index parameter obtained in step 1, calculate the customer peak avoidance index initial value, the formula is as follows:

In the formula: P _ll-mor , P _ll-mid , P _{ll-eve —represent} the limitable loads of morning peak, mid-peak and evening peak respectively;

E _q ——Increased value of unit electricity production;

T _q —— profit and tax per unit of electricity;

f _l — load fluctuation rate;

P _q —— customer electricity price.

2.2) Arrange the initial value data of peak avoidance index of all customers, use K-means cluster analysis method, take K=5. The initial value of the peak avoidance index was divided into 5 grades, namely excellent, good, moderate, general and poor.

2.3) Determine whether the nature of the customer's production is continuous.

2.4) The initial value of the peak avoidance index of the non-continuous user is reduced by one level, and the peak avoidance index is calculated; for the continuous user, the value of the peak avoidance index remains unchanged. The specific calculation example is as follows:

Assuming that the initial value of a customer’s peak avoidance index is m, which is in the first level range, it is judged that the customer is a discontinuous user, then

Assuming that a customer's initial peak avoidance index is n, which is in the first gear, and it is judged that the customer is a continuous user, the peak avoidance index remains unchanged.

Table 2 Evaluation index table of peak avoidance index effect

Avoidance index range Evaluation of peak avoidance effect grade a-A excellent first gear b-B good second gear c-C Moderate third gear d-D generally fourth gear e-E Difference fifth gear

Step 3. Calculation of the time off index.

3.1) Calculate the initial value of the rest index, the formula is as follows: initial value of the rest index = limitable load.

3.2) Arrange the initial value data of all customers' time-off index, use K-means cluster analysis method, and take K=5. The initial value of the rest index is divided into 5 grades, which are excellent, good, moderate, general and poor.

3.3) According to the customer's typical load curve, calculate the customer's weekly rest rate, the formula is as follows:

${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; l</span>}}}$

In the formula: Q _wd - the arithmetic mean of the total load from Monday to Friday;

Q _We - the arithmetic mean of the total load on Saturday and Sunday;

Q _sl — indicates the total load of security load in one day;

3.4) The initial value of the customer's rest index with a weekly rest rate greater than 0.3 and less than 0.7 will be reduced by two steps; the initial value of the client's rest index with a weekly rest rate greater than or equal to 0.7 will be reduced by one step, and the weekly rest rate will be less than or equal to The initial value of the customer adjustment index of 0.3 remains unchanged. The specific downshift algorithm is the same as the peak avoidance index.

Step 4: Calculate the time-staggered index.

4.1) Calculate the initial value of the time error index, the formula is as follows:

4.2) Calculate the initial value of time-staggered index of all customers, use K-means cluster analysis method, take K=5. The timing index is divided into 5 grades, which are excellent, good, moderate, general and poor.

4.3) The result of the timing index will not be adjusted, and it will be the same as the initial value.

As shown in Figure 3, the judgment of the user's production continuity is realized in the following way.

The production nature of enterprise customers is divided into continuous and discontinuous. Continuous production enterprises generally operate continuously in three shifts, with large power loads and relatively stable load curves. Discontinuous production enterprises generally operate discontinuously in day shifts, with small power loads and large fluctuations in load curves.

By classifying the typical curves of customers, customers are divided into continuous and discontinuous production enterprises. Firstly, the time axis (00:00-24:00) within 1 day (24 hours) is divided into 48 time periods, namely T ₁ , T ₂ , . . . , T ₄₈ . Taking the current orderly electricity consumption time as the starting point, select the electricity consumption data of customers in the past 3 months as the sample database (excluding holidays). The calculation method of the user's production property is as follows:

(1) According to the sample database data, the customer load data is superimposed and averaged, and fitted to a typical customer load curve for one day.

(2) Taking the value of 20% of the maximum load of the typical load curve as the first level, the load curve is divided into 5 levels, and the lowest level is 20% of the maximum load.

(3) Count the points where the load value in the typical load curve is less than the load of the lowest level interval. Determine whether the ratio of the number of load points to the total number of points (T ₁ , T ₂ , ..., T ₄₈ ) is less than 10%, if less than 10%, the customer's production is continuous, otherwise it is non-continuous production.

The present invention makes up for the domestic extensive and singular evaluation of the implementation effect of customers' orderly electricity faults and peak avoidance measures, and provides a multi-dimensional and quantifiable method for evaluating the effects of orderly electricity faults and peak avoidance measures for power supply enterprises. Specifically, the artificial experience judgment mode is discarded, and various factors such as customer power-limiting capacity, economic impact, power impact, and tax impact are integrated into three major indicators: peak avoidance index, holiday adjustment index, and time-staggered index. The size of the index is used as the index. Evaluate the basis for the customer's electricity curtailment effect, greatly improve the efficiency of orderly power consumption plan preparation, and improve the scientificity and fairness of the evaluation of the electricity curtailment effect.

Some nouns involved in the present invention are explained below.

Orderly use of electricity: orderly use of electricity refers to the management work of controlling part of the demand for electricity according to the law through administrative measures, economic means, and technical methods to maintain the order and stability of power supply and consumption under the circumstances of insufficient power supply and emergencies. Take a series of measures such as staggering peaks, avoiding peaks, taking turns, giving up power, and limiting power, to avoid unplanned power cuts, standardize the order of power consumption, and minimize the adverse impact of power supply and demand contradictions on society and enterprises.

Daily maximum load: the maximum value of the power supply load at each half hour of the day.

Peak staggering: Peak staggering refers to the transfer of electricity load during peak hours to other hours, usually without reducing electricity use.

Peak avoidance: Peak avoidance refers to cutting, interrupting or stopping electrical loads during peak hours, usually reducing electrical energy use.

Power rationing: Power rationing refers to restricting some or all of the electricity demand of certain users during a specific period of time.

Shut-up: Shut-up refers to dispatching orders issued by dispatching agencies at all levels to cut off part of the electricity load.

Power gap: The power gap refers to the sum of load shifting, peak avoidance, power cut, and switching load of all users at a certain point in time.

Early warning signal: According to the difference in the proportion of the power gap to the maximum electricity demand in the current period, the early warning signal is divided into four levels:

Grade I: extremely serious (red, above 20%);

Grade II: severe (orange, 10%-20%);

Grade III: heavier (yellow, 5%-10%);

Grade IV: fair (blue, below 5%).

Limitable load: Limitable load refers to the "reduced" load that the user only keeps the guaranteed load and shuts down the equipment during the peak period of power.

Security load: Security load refers to the power load required to ensure the safety of people and property in the power-using place.

Claims (3)

1. Based on orderly electricity consumption, the method for evaluating the effects of customer faults and off-peak electricity curtailment is characterized by: The peak avoidance index is used to represent the effect of the user's implementation of the peak avoidance measures for power reduction, the adjustment index is used to represent the power reduction effect of the user's implementation of the adjustment and rest measures, and the time-staggered index is used to represent the effect of the user's implementation of the time-staggered measures; the specific calculation process is divided for 4 steps, Step 1. Calculating the relevant parameters related to the peak avoidance index, the holiday index, and the time-staggered index are as follows: Step 1.1. Draw a typical customer curve: select the load data of the customer for at least 3 months in the near future, and perform superimposed average calculation on the daily load curve during this period, and form a typical customer load curve by fitting; Step 1.2. Calculate the ratio of the customer's annual production added value to the annual electricity consumption to obtain the customer's unit electricity production added value; Step 1.3. Calculate the ratio of the customer's annual profit and tax from the government to the annual electricity consumption to obtain the customer's unit electricity profit and tax; Step 1.4. Calculate the ratio of the average value of the customer load to the standard deviation from 8:00 to 22:00 on a typical day to obtain the customer load fluctuation rate; the calculation formula of the load fluctuation rate is as follows: ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}<span\; class="notranslate">\; =</span>\frac{\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$ In the formula: f _l - load fluctuation rate; P _i - typical load value at the i-th time point; σ - standard deviation of loading; μ - the mean value of the load; N - the number of said time points; Step 1.5, Calculation of customer security load: Calculate the actual load data within the time period selected by the customer in step 1.1, and calculate the arithmetic mean value of the smallest 200 load points, the formula is as follows: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 200</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; 200</span>}}$ In the formula: p _sl - security load; p _mini - the ith smallest load point; Step 1.6. Calculate the customer's limitable load: According to the customer's typical load curve drawn in step 1.1, the calculation formula is as follows: P _ll-mor = (P _pl-mor -P _sl )×δ _p P _ll-mid = (P _pl-mid -P _sl )×δ _p P _ll-eve = (P _pl-eve -P _sl )×δ _p In the formula: P _ll-mor , P _ll-mid , and P _ll-eve represent the limitable loads of the morning peak, waist peak, and evening peak respectively; P _sl - security load; P _pl-mor , P _pl-mid , and P _pl-eve represent the peak loads during morning peak, waist peak, and evening peak, respectively; δ _p - simultaneity rate; Step 2. Calculation of avoidance index: Step 2.1. Calculate the initial value of the customer peak avoidance index according to the customer peak avoidance index parameters obtained in step 1. The formula is as follows: In the formula: P _ll-mor , P _ll-mid , and P _ll-eve represent the limitable loads of the morning peak, waist peak, and evening peak respectively; E _q - the added value of electricity production per unit; T _q - profit and tax per unit of electricity; f _l - load fluctuation rate; P _q - customer electricity price; Step 2.2, organize the initial value data of peak avoidance index of all customers, use K-means clustering analysis method, take K=5, divide the initial value of peak avoidance index into 5 grades, respectively excellent, good, moderate, general and poor; Step 2.3, judging whether the nature of the customer's production is continuous; Step 2.4, the initial value of the peak avoidance index of the non-continuous user is reduced by one gear, and the peak avoidance index is calculated; for the continuous user, the peak avoidance index value remains unchanged; Step 3. Calculation of time off index: Step 3.1, calculate the initial value of the rest index value, the formula is as follows: initial value of the rest index = limitable load; Step 3.2, sort out the initial value data of all customers’ time off index, use K-means cluster analysis method, take K=5, divide the initial value of time off index into 5 grades, respectively excellent, good, moderate, general, and poor; Step 3.3. According to the typical load curve of the customer, calculate the weekly rest rate of the customer, the formula is as follows: ${\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; e</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; l</span>}}}$ In the formula: Q _wd - the arithmetic mean of the total load from Monday to Friday; Q _we - the arithmetic mean of the total load on Saturday and Sunday; Q _sl - the total load of security load in one day; Step 3.4: Downgrade the initial value of the customer’s rest index by two steps for the weekly rest rate greater than 0.3 and less than 0.7; downgrade the initial value of the client’s rest index for the weekly rest rate greater than or equal to 0.7; for the weekly rest rate less than or equal to The initial value of the customer schedule index equal to 0.3 remains unchanged; Step 4. Calculation of time error index: Step 4.1, calculate the initial value of the time error index, the formula is as follows: initial value of the time error index = limitable load; Step 4.2, counting the initial values of all customers' timing index, using K-means cluster analysis method, taking K=5, dividing the timing index into 5 grades, which are respectively excellent, good, moderate, general, and poor; In step 4.3, the result of the time-staggered index is not adjusted, and is the same as the initial value. 2. As claimed in claim 1, based on orderly power consumption, the method for evaluating the effect of customer errors, peak avoidance and curtailment, is characterized in that the simultaneous rate δ _p is set to 0.8. 3. As claimed in claim 1, the method for evaluating the effect of customer faults and off-peak power cuts based on orderly power consumption is characterized in that, the morning peak, waist peak, and evening peak periods are respectively: 8:00-12:00 , 12:00-17:00, 17:00-22:00.